How Does AI Influence and Mitigate Operational Risk in the Financial Institution Assessment Answer

Research Question

How Does AI Influence How to Mitigate Operational Risk in the Financial Institution?

In financial institutes such as banks, risk management is critically important. Organizations generate profitability by taking financial risks, which is important for survival. Risk management enables organizations to control the process by identifying losses with more predictability. In financial institutions, the consequences of operational risks can impact the business for financial and reputational loss. Artificial intelligence has the potential to determine operational risks by using historical and ongoing data. It can help the organizations procure the resources and support strategic goals throughout the operations for improved outcomes (Mhlanga, 2020).

Financial institutes and their operational risks are assessed in existing literature, and solutions such as strategic models and operational redesigns are introduced. However, the recent development of artificial intelligence has grabbed the industry's attention. This technology can help bankers and governmental bodies to obtain more precise data for decision-making. Artificial intelligence allows financial institutes to use computers and powerful software to make related decisions (Mou, 2019). From marketing to e-commerce, logistics and manufacturing, artificial intelligence is proving the capabilities for improved integration into modern financial institutes.

The emerging concept of artificial intelligence is reshaping business operations by redefining the use of data in business. The research matters for the financial institutes as they can explore how the technology can help them to reduce the financial and non-financial costs associated with operational risks and how they can automate the risk management approach to a higher degree. It also opens an area to explore how the organization can drive competitive advantages by using artificial intelligence in operations. The research can answer the questions regarding required capabilities, constraints in the adoption of technology and the overall influence of technology if it is adopted or discarded in context.

Proposed Research Plan

Research Paradigm

The interpretivism paradigm guides the research because human interest is integrated to interpret the elements of the study. This paradigm is based on the ontological assumptions that social constructions such as shared meaning, consciousness, instruments and language are enablers for access to reality (Alharahsheh and Pius, 2020). It is based on concepts of positivism and therefore focuses on qualitative analysis. This paradigm also guides that understanding the world and reality is a subjective experience of the individual. It means surveys and interviews with human participants to understand their perspective and experience with the subject, such as artificial intelligence (Pham, 2018).

According to interpretive epistemology, there is important to understand how the intersubjective meaning of a structure for the domain of interest is reconstructed. It means that individual constructs understanding and knowledge when interpreting experience with the world. It also rejects the objectivism that knowledge is identified and absorbed as it is. Therefore, it is assumed to extract alternative or maximum knowledge from the known facts (Berryman, 2019). Considering this paradigm and related assumptions, it is identified that research on artificial intelligence to mitigate operational risks can improve the understanding of what is wrong and right in the operations of financial institutes.

Data Collection Methods

The study uses a qualitative data collection method, which uses beliefs and experiences, interactions, behaviours and attitudes of people for non-numerical data. Google Scholar is a popular source for obtaining qualitative data. A large set of published literature reviews and papers can be identified by filtering the data by year and adding keywords in the search field. Literature reviews can help to understand the knowledge investigated by others in the domain so that they provide a critical evaluation of the material while developing an improved understanding of the domain (Hennink et al., 2020). A literature review is a good source to understand the existing work so that the research can examine the boundaries and limitations to explore further. The qualitative method not only helps to understand the topic but also allows for the investigation of the theories and concepts so that the gap with requirements can be identified and dimensions for the new research can be initiated (Taylor et al., 2015). It uses the open questions of previous research works and works toward the induction of new knowledge.

Qualitative research design focuses mainly on two things: why and how. For example, it aims to identify how AI influences the way to mitigate operational risks in various financial institutes. Therefore, this research design is subjective regarding data collection and processing. The presented case study considers mainly two approaches for qualitative research. First, historical data is used to understand how operational risks are managed or mitigated in financial institutes. It helps to develop an understanding of existing practices. Second, phenomenology is considered to determine how individuals experience the phenomenon and what their feelings are about it (Merriam and Tisdell, 2015). It helps to understand their perspective and experience regarding operational risk and artificial intelligence. Therefore, the interview is the foremost method for qualitative research. In an interview, major participants are members of financial institutes directly or indirectly involved in mitigating operational risks. It further includes the technical members managing the operational risks under the guidance of managers.

Research interviews with participants so that the required information can be collected and processed to conclude. The interview allows the researchers to prompt the participants and obtain their input for required details (Mann, 2016). The structured or focused interview is preferred for the study because such types of interviews are flexible but allow minimal scope for participants to obtain and analyze the results. It means questions are pre-decided and used to maintain uniformity in all interviews. The study uses open-ended questions in an interview to enable the participants to explore their experiences. In a comparison of an unstructured and semi-structured interview, there is a focus on accuracy in responses. Standardizing questions for all participants also makes the process easy and effective, even when dealing with large sample sizes (Roulston and Choi, 2018). Also, the scope of the information is pre-defined so that better information can be collected from the participants. It has reliable, and it is easy to identify the margin of error due to the informal relationship between the researcher and the respondent. However, its accuracy overpowers the details and limits the scope of the assessment. Further, there is no focus on interest in the conversation, and respondents are forced to provide their responses on the selected areas (Bryman, 2017). Unstructured and semi-structured interviews may add flexibility, but they become unstandardized and therefore, data collection and analysis are complex.

Interviews are conducted with managers in financial institutions. There are mainly three reasons to select managers as potential participants in the interview. First, managers have an effective understanding of the operational risks, and they closely work to make the organization competitive with others. Second, managers can provide insights into operations and enable understanding regarding data and the scope of application for artificial intelligence. It means more meaningful and required data can be considered in decision-making (Fountaine et al., 2019). Media stories are used to take examples of artificial intelligence in mitigating operational risks as adopted by large organizations. However, there is no information collected regarding formal and business documents.

It is hard to collect all significant data through interview questions before interviewing. There is a need to understand the topics such as operational risks, the working of financial institutes, majorly adopted risk management frameworks and contributions artificial intelligence technology can make (Tracy, 2019). Therefore, all the information is collected using a literature review and interview questions. As there is a significant contribution of information obtained from participants, the process of to interview is required to standardize to foster accuracy in analysis.

Sampling Procedures

Theoretical sampling is a type of sampling technique that is not bounded by the limitations of priori-selections. It focuses on the joint collection and analysis of data to determine the next data in priority and how to find further data development of theories (Gentles et al., 2015). In the current context, it is closely related to the research question as there is a focus on finding how artificial intelligence contributes to mitigating operational risks. Therefore, collecting data from the right people is important to reach a valid conclusion. For instance, organizational managers and information technology officers are the most knowledgeable people to obtain the required data. Further, the owners or members of the board of directors also motivate people to consider the sampling procedure.

Participants are selected randomly using the available information on the internet for large financial institutions. Later, they are invited to participate in an interview by answering the questions on their system. In this overall process, 15 days are offered to them so they can submit a complete response. The questions are categorized and written with a focus that they are easy to understand for participants. The following question is also maintained. For instance, the initial questions were related to the participant's knowledge and experience of operational risks and artificial intelligence. In later questions, there is an exploration of their experience and approaches to the risks and how they perceive the capabilities of artificial intelligence to mitigate operational risks.

 A theoretical situation may occur in the sampling procedure, especially when participants are unclear. For instance, the research question is related to both stakeholders: a manager working at the executive level and managers working at the operational level. Therefore, it becomes hard to determine whether the manager or the leaders should contribute (Nelson, 2017). Similarly, the executive leaders may not have enough understanding of artificial intelligence and they may not contribute effectively to understanding the influence of technology. Such situations occur when there are limited factors to classify the participants and information. In the case of information, it might be possible that participants' responses in an interview may not vary significantly, and it becomes hard to ensure accuracy in the outcome.

The stratified sampling approach is feasible for the study because it helps select the participants from all the groups. It can help to avoid theoretical or purposeful situations. It allows a selection of participants, such as the general manager, technology manager and leaders and assesses the results based on their ratio to an overall number of participants (Sharma, 2017). Compared to simple random techniques and other methods, ensuring the contribution of all groups' representatives is simple and effective.

Analysis Strategy

In the interview process, qualitative data is processed, and therefore, there is a need for a systematic strategy to analyze the data. The core purpose is to capture the patterns and themes and interrelate them to conclude. For example, the responses to each question can be analyzed, and key factors, impacts and outcomes can be thematically arranged to analyze the rest of the responses. Each theme or pattern can be identified using content and thematic analysis, and it can be ensured that all the major things are documented (Neuendorf, 2018). It can help structure the descriptive data using keywords in a table.

Content analysis is a research strategy used to analyze qualitative data to identify patterns, themes, and meanings within the data. The content analysis aims to identify and describe the underlying meanings and patterns in the data. It includes identifying the research question, data collection, creating a coding scheme and coding the data, analyzing and interpreting the data and documenting the findings. Here, coding means standardizing the presentation of data for further utilization in the research (Humble and Mozelius, 2022). Similarly, thematic analysis is a strategy for analyzing qualitative data that involves identifying patterns, themes, and categories within the data. It is a flexible and iterative approach that allows researchers to identify and interpret the patterns and meanings that emerge from the data (Vaismoradi et al., 2016). It also has similar types of steps, but it focuses on identifying themes and patterns in data. These strategic components ensure that the required information is collected and the qualitative information is easy to analyze.

The selected strategy is good for getting the required data for the research question. It allows the categorization of the data by identification of patterns and themes. It also establishes aggregated dimensions and helps to determine how the influencing factors of AI are interconnected. It also leads to theoretical categories to examine how technology can contribute (Neuendorf, 2018). Therefore, the strategy guides overlapping data collection and analysis and helps to develop unique data to support the research work.

Criteria for Judging the Quality

Assessing the quality of qualitative research can be challenging, as there is no universally agreed-upon standard for what constitutes "good" qualitative research. However, several criteria can be used to evaluate the quality of qualitative research.

Credibility

Credibility refers to the extent to which the research findings are believable and trustworthy. To assess credibility, consider whether the research methods were appropriate for the research question, whether the data were collected and analyzed rigorously, and whether the data support the findings.

Transferability

Refers to the extent to which the research findings can be applied to other contexts or populations. To assess transferability, consider whether the researchers have provided sufficient contextual information and whether the findings are relevant to other settings or populations (Jarzabkowski et al., 2021).

Dependability

Refers to the stability and consistency of the research findings over time. To assess dependability, consider whether the research methods and findings are consistent with other research in the field.

Confirmability

 Confirmability refers to the extent to which the research findings are grounded in the data and not influenced by the researchers' biases or assumptions. To assess confirmability, consider whether the researchers have provided a clear and transparent account of their methods and whether they have taken steps to minimize their own biases and assumptions (Flick, 2018).

Reflexivity

Refers to the researcher's awareness of their positionality and how this may have influenced the research process and findings. To assess reflexivity, consider whether the researchers have reflected on their biases and assumptions and how these may have influenced the research.

Saturation

Saturation refers to the point at which new data no longer provide new insights or themes. To assess saturation, consider whether the researchers have collected enough data to ensure the findings are comprehensive and representative.

Ethics

Ethics refers to the extent to which the research has been conducted ethically and responsibly. To assess ethics, consider whether the researchers have obtained informed consent from the participants, protected their confidentiality and privacy, and minimized potential harm or discomfort to the participants (Wa-Mbaleka, 2017).

Overall, assessing the quality of qualitative research requires a nuanced and critical approach that considers a range of criteria. By carefully evaluating these criteria, researchers and readers can ensure that qualitative research is high quality and can be used to inform practice and policy.

Critical Evaluation and Justification

Critical Evaluation of Methods not Chosen

Evaluation of Qualitative Methods

Qualitative methods such as interviews or focus groups could be used to collect data from managers about their experiences and mechanisms for mitigating operational risk. The data collected from these methods could be analyzed using thematic or content analysis to identify patterns, themes, and categories related to managers' experiences and approaches to dealing with operational risks. Qualitative methods have strengths in several terms. For example, qualitative methods can provide rich and detailed data about people's experiences, attitudes, and beliefs that quantitative methods cannot capture (Brannen, 2017). Second, these are flexible and can be adapted to suit the research question and the participants' needs. Third, it can provide a deeper understanding of the context in which people's experiences and behaviours occur, which can be important for developing operational approaches.

Further, can give voice to marginalized or underrepresented groups, allowing their perspectives to be heard and valued. However, qualitative methods can be subjective, as they rely on researchers' interpretations of data. These are often used to gain an in-depth understanding of a particular context or population, but the findings may not be generalizable to other contexts or populations. These can be time and resource-intensive, requiring extensive training and preparation, and may require a larger sample size to achieve saturation (Taylor et al., 2015). Qualitative methods can be influenced by researcher bias, as researchers' perspectives and assumptions can shape the data collection and analysis.

Using qualitative methods to investigate research questions can provide rich and detailed insights into people's experiences and perspectives. Still, researchers should also consider these methods' limitations and potential biases when interpreting and reporting their findings.

Evaluation of Quantitative Methods

Quantitative methods such as surveys or questionnaires could be used to collect data from a large sample of managers about their experience with the capability of AI in operational risk mitigation. The data collected from these methods could be analyzed using statistical methods to calculate prevalence rates and identify burnout-related factors. Quantitative methods are objective and less prone to researcher bias, as they rely on standardized measurements and statistical analysis. These are often used to gather data from a large and representative sample, which can increase the generalizability of the findings to the broader population. These efficient methods can collect data from a large sample in a relatively short period, making them suitable for studies with large sample sizes (Brannen, 2017). These methods use standardized measures and procedures, which makes it easier for other researchers to replicate the study and validate the findings. However, quantitative methods often prioritize breadth over depth, meaning that they may not capture individuals' or groups' rich and nuanced experiences. These methods can reduce complex phenomena into numerical values, which may oversimplify or obscure the complexity of the phenomenon being studied. These methods can be susceptible to measurement error, leading to inaccurate results if the measures used are not reliable or valid (Queirós et al., 2017). Quantitative methods may be limited to studying variables that can be easily quantified and measured, which can be a disadvantage if the phenomenon being studied is multifaceted or difficult to define.

Using quantitative methods to investigate research questions can provide reliable and objective data that can be generalized to the broader population. However, researchers should also consider the limitations of these methods, such as their lack of depth, reductionism, and potential for measurement error. Additionally, researchers should consider combining quantitative and qualitative methods to comprehensively understand the research question.

Qualitative and quantitative research methods are two broad approaches to collecting and analyzing data in research. While both methods have strengths and weaknesses, their aims, methods, and data analysis techniques differ (Patten, 2017).

Qualitative research aims to understand social phenomena in a natural setting and explore the meaning people attribute to their experiences. It typically involves collecting data through interviews, focus groups, and observations and analyzing it using thematic or grounded theory methods. Qualitative research can provide rich and detailed data about people's experiences, attitudes, and beliefs, but its findings may not be generalizable to larger populations.

Quantitative research aims to measure social phenomena through numerical data and statistical analysis. It typically involves collecting data through surveys, experiments, or observations and analyzing it using statistical methods such as regression or factor analysis (Greenfield and Greener, 2016). Quantitative research can provide objective and generalizable data about the prevalence of phenomena and the relationships between variables, but it may oversimplify complex phenomena and miss important contextual factors.

In conclusion, qualitative and quantitative methods have their strengths and weaknesses, and the choice of method depends on the research question, aims, and context. Researchers can also consider combining qualitative and quantitative methods to comprehensively understand the research question.

Justification for the Selected Method

Qualitative research is a valuable method for exploring complex social phenomena and understanding the meaning that people attribute to their experiences. It involves collecting data through open-ended interviews, focus groups, and observations and analyzing it using thematic or grounded theory methods. Here are some justifications for selecting the qualitative method in research:

Exploration of complex social phenomena: Qualitative research is particularly useful for exploring complex social phenomena that cannot be easily measured or quantified. Qualitative methods allow researchers to delve deeply into participants' experiences, attitudes, and perspectives and gain a rich understanding of the phenomenon being studied (Taylor et al., 2015).

Emphasis on context and meaning: Qualitative research focuses on understanding the context and meaning people attribute to their experiences. This approach allows researchers to understand the social world and identify factors that quantitative methods may overlook.

Flexibility and openness to new perspectives: Qualitative research is flexible and allows researchers to adapt their methods and questions as new insights emerge. It is also open to new perspectives and allows participants to shape the research process and contribute to the findings.

Opportunities for participant voice and engagement: Qualitative research values the voices and experiences of participants and provides opportunities for them to share their experiences and contribute to the research process (Greenfield and Greener, 2016). This approach can empower participants and promote social change.

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